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Graph Name Retrieved From View
workflow graph MAnorm SE - quantitative comparison of ChIP-Seq single-read data

What is MAnorm? -------------- MAnorm is a robust model for quantitative comparison of ChIP-Seq data sets of TFs (transcription factors) or epigenetic modifications and you can use it for: * Normalization of two ChIP-seq samples * Quantitative comparison (differential analysis) of two ChIP-seq samples * Evaluating the overlap enrichment of the protein binding sites(peaks) * Elucidating underlying mechanisms of cell-type specific gene regulation How MAnorm works? ---------------- MAnorm uses common peaks of two samples as a reference to build the rescaling model for normalization, which is based on the empirical assumption that if a chromatin-associated protein has a substantial number of peaks shared in two conditions, the binding at these common regions will tend to be determined by similar mechanisms, and thus should exhibit similar global binding intensities across samples. The observed differences on common peaks are presumed to reflect the scaling relationship of ChIP-Seq signals between two samples, which can be applied to all peaks. What do the inputs mean? ---------------- ### General **Experiment short name/Alias** * short name for you experiment to identify among the others **ChIP-Seq SE sample 1** * previously analyzed ChIP-Seq single-read experiment to be used as Sample 1 **ChIP-Seq SE sample 2** * previously analyzed ChIP-Seq single-read experiment to be used as Sample 2 **Genome** * Reference genome to be used for gene assigning ### Advanced **Reads shift size for sample 1** * This value is used to shift reads towards 3' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **Reads shift size for sample 2** * This value is used to shift reads towards 5' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **M-value (log2-ratio) cutoff** * Absolute M-value (log2-ratio) cutoff to define biased (differential binding) peaks. Default: 1.0 **P-value cutoff** * P-value cutoff to define biased peaks. Default: 0.01 **Window size** * Window size to count reads and calculate read densities. 2000 is recommended for sharp histone marks like H3K4me3 and H3K27ac, and 1000 for TFs or DNase-seq. Default: 2000

 https://github.com/datirium/workflows.git

Path: workflows/manorm-se.cwl

Branch/Commit ID: 4ab9399a4777610a579ea2c259b9356f27641dcc
workflow graph bam_readcount workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/bam_readcount.cwl

Branch/Commit ID: ddd748516b25256a461ea9277303406fa2759b00
workflow graph rnaseq-star-rsem-pe.cwl

https://github.com/pitagora-network/DAT2-cwl.git

Path: workflow/rna-seq/rnaseq-star-rsem-pe/rnaseq-star-rsem-pe.cwl

Branch/Commit ID: 0cd20e1be620ae0817a1aa4286d73b78c89809f0
workflow graph umi molecular alignment workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/molecular_alignment.cwl

Branch/Commit ID: 479c9b3e3fa32ec9c7cd4073cfbccc675fd254d9
workflow graph tt_univec_wnode.cwl

https://github.com/ncbi/pgap.git

Path: task_types/tt_univec_wnode.cwl

Branch/Commit ID: 92118627c800e4addb7e29b9dabcca073a5bae71
workflow graph AltAnalyze CellHarmony

AltAnalyze CellHarmony ======================

 https://github.com/datirium/workflows.git

Path: workflows/altanalyze-cellharmony.cwl

Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc
workflow graph cnv_exomedepth

CNV ExomeDepth calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/abstract_operations/subworkflows/cnv_exome_depth.cwl

Branch/Commit ID: 3f6a871f81f343cf81a345f73ff2eeac70804b8c
workflow graph workflow-htcondorcern.cwl

https://github.com/reanahub/reana-demo-root6-roofit.git

Path: workflow/cwl/workflow-htcondorcern.cwl

Branch/Commit ID: 2b79f1c4aea6981845647b1ba880832288eaeb88
workflow graph cnv_manta

CNV Manta calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/abstract_operations/subworkflows/cnv_manta.cwl

Branch/Commit ID: 3f6a871f81f343cf81a345f73ff2eeac70804b8c
workflow graph THOR - differential peak calling of ChIP-seq signals with replicates

What is THOR? -------------- THOR is an HMM-based approach to detect and analyze differential peaks in two sets of ChIP-seq data from distinct biological conditions with replicates. THOR performs genomic signal processing, peak calling and p-value calculation in an integrated framework. For more information please refer to: ------------------------------------- Allhoff, M., Sere K., Freitas, J., Zenke, M., Costa, I.G. (2016), Differential Peak Calling of ChIP-seq Signals with Replicates with THOR, Nucleic Acids Research, epub gkw680.

 https://github.com/datirium/workflows.git

Path: workflows/rgt-thor.cwl

Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc